Personalization of the relapse probability and prevention of post-surgery ovarian endometriosis

Introduction. Endometriosis affects about 10 % of reproductive age women and is characterized by a chronic relapsing course. The probability of endometriosis relapse following disease surgical treatment remains very high, which underlies a need to propose reliable, personalized approaches predicting relapse risk for choosing optimal methods to prevent relapse development. Aim: to develop a personalized approach for assessing probability of emergence and measures to prevent ovarian endometriosis relapse following surgical intervention.

and Methods. The prospective study examined 148 patients with unilateral endometrioma following surgical treatment; subsequently, the patients were divided into two groups. The main group consisted of 59 patients with endometriosis relapse, the control group included 89 patients without relapse. To create a model for predicting endometriosis relapse, there were used variables referred to categories A, B and C evidence, which increase a risk of developing post-surgery endometriosis. Treatment adherence was assessed using the AQ-25 questionnaire, the stress level – according to the Holmes-Rage method. The relative risk (RR) calculation was used while model constructing. Results. Assessing RR magnitude, there was quantitated a significance of qualitative predictors for endometriosis relapse showing that in case smoking it was 2.4, stress – 2.0, young age (under 25 years) – 2.4, treatment adherence – 3.2, incomplete removal of endometriosis foci – 3.1, and also depended on body mass index (RR = 1.6). Relapse occurred in 18 patients during the first year post-surgery coupled to the maximum number (8.4 ± 0.5) of risk factors. During the following year, 13 new relapse cases were detected coupled to 5.3 ± 0.4 risk factors. During the third year of observation, the number of relapses increased by 12 cases, and the number of risk factors not exceeding 4.6 ± 0.2. Based on the data obtained, the risk index (RI) = n×x (conventional units) was introduced to develop a quantitative approach to assessing the specific timing of relapse development over 4 years taking into account the adverse impact of "n" risk factors on the body over "х" years. Conclusion. In a personalized approach for assessing the risk of endometriosis relapse, the significance of evidence-based medicine qualitative predictors requires quantitative clarification. The proposed RI allows predicting specific relapse timing with > 80 % reliability. The proposed method allows synchronizing intensified preventive measures together with the period of maximum risk of disease development. About the Authors E. Yu. ZhilnioRussian Federation Ekaterina Yu. Zhilnio - MD. 112 Bolshaya Kazachya Str., Saratov 410012; 19 Kholzunov A.I. Str., Saratov 410017 I. A. Salov Russian Federation Igor A. Salov - MD, Dr Sci Med, Prof. 112 Bolshaya Kazachya Str., Saratov 410012 I. V. Naumova Russian Federation Iuliia V. Naumova - MD, PhD. 19 Kholzunov A.I. Str., Saratov 410017

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What are the new findings? ► Quantitatively assessed significance of qualitative predictors for ovarian endometriosis relapse is presented that rely on the relative risk magnitude. ► The risk index (RI) was developed that takes into account the adverse impact of "n" risk factors on the body over "х" years, and allows to predict specific timing of endometriosis relapse following surgery over the next 4 years with ≥ 80 % reliability. How might it impact on clinical practice in the foreseeable future? ► The presented method to predict the timing of endometriosis relapse allows synchronizing intensified preventive measures together with the period of maximum risk of disease development. Review For citations: Zhilnio E.Yu., Salov I.A., Naumova I.V. Personalization of the relapse probability and prevention of post-surgery ovarian endometriosis. Obstetrics, Gynecology and Reproduction. 2025;19(5):654-666. (In Russ.) https://doi.org/10.17749/2313-7347/ob.gyn.rep.2025.649 JATS XML This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.